Melanoma Recognition

Michal Haindl

and Pavel

Zid

The Institute of Information Theory and Automation, Czech Academy of Sciences,

Pod Vod

arenskou v

ı 4, Prague, Czech Republic

Keywords:

Skin Cancer Recognition, Melanoma Detection, Circular Markov Random Field Model.

Abstract:

Early and reliable melanoma detection is one of today’s signiﬁcant challenges for dermatologists to allow

successful cancer treatment. This paper introduces multispectral rotationally invariant textural features of

the Markovian type applied to effective skin cancerous lesions classiﬁcation. Presented texture features are

inferred from the descriptive multispectral circular wide-sense Markov model. Unlike the alternative texture-

based recognition methods, mainly using different discriminative textural descriptions, our textural representa-

tion is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy

for skin lesion categorization. We tested our classiﬁer on the open-source dermoscopic ISIC database, contain-

ing 23 901 benign or malignant lesions images, where the classiﬁer outperformed several deep neural network

alternatives while using smaller training data.

1 INTRODUCTION

Diagnosing skin diseases is complicated. At least

3,000 identiﬁed varieties of skin diseases (Kawahara

and Hamarneh, 2019) with a prevalence that varies

by condition. Skin diagnosis is usually determined

using a biopsy, which, however lengthy, costly, un-

comfortable, and may introduce potential infectious

complications to the patient. Image-based automatic

skin diagnosis potentially avoids all these difﬁculties.

Automatic skin cancer recognition is a challenging

but proper application that can help dermatologists

in early cancer detection. Melanoma accounted for

41% skin-related deaths in the USA in 2013 (Kawa-

hara and Hamarneh, 2019) where every hour one per-

son dies from melanoma, while the highest melanoma

rate is in Australia (34 000 skin cancers every year)

and New Zealand (Zhang et al., 2020). Thus the early

melanoma diagnosis is of the utmost importance. Di-

agnosis of skin lesion type is a challenging task even

for a skilled dermatologist. The use of dermoscopy

imaging devices signiﬁcantly improved the quality of

early melanoma detection. Further improvement is

possible by the use of computer-assisted diagnosis.

Automatic skin lesion categorization allows iden-

tiﬁcation or learning of skin lesion types possible

without speciﬁc medical knowledge. Recent stud-

https://orcid.org/0000-0001-8159-3685

https://orcid.org/0000-0001-8249-1701

ies show that recognition systems can match or even

outperform clinicians in the diagnosis of individual

skin lesion images in controlled reader studies (Kawa-

hara and Hamarneh, 2019; Rotemberg et al., 2021).

Results comparison is difﬁcult due to different data

sets or their subset used in different studies and of-

ten not appropriately described. (Ballerini et al.,

2013) reached 74% accuracy for ﬁve classes using

the hierarchical k-NN classiﬁer using mean and co-

variance color matrices, and 12 features derived from

gray-level co-occurrence matrices (3888 features) on

their dataset with 960 images acquired from Canon

EOS 350D SLP camera. Authors (Gomez and Her-

rera, 2017) used an SVM classiﬁer with histogram

and gray-level co-occurrence matrices-based features

and reached 70% accuracy on UDA, MSK, and

SONIC data sets. The gray level co-occurrence fea-

tures are similarly used as texture features in (Shak-

ourian Ghalejoogh et al., 2019).

Several algorithms (Akram et al., 2020; Ballerini

et al., 2013; Esteva et al., 2017; Favole et al., 2020;

Gessert et al., 2020; Hosny et al., 2020; Kawahara

et al., 2016; Khan et al., 2021; Mahbod et al., 2019;

Nida et al., 2019; Tschandl et al., 2019; Zhang et al.,

2019; Zhang et al., 2020) are using convolutional neu-

ral networks (CNN). (Kawahara et al., 2016) reached

85% accuracy for ﬁve classes on AlexNet (4096 fea-

tures) on the Dermoﬁt (Ballerini et al., 2013) 1300 im-

ages. (Esteva et al., 2017) used GoogleNet Inception

722

Haindl, M. and Žid, P.

Melanoma Recognition.

DOI: 10.5220/0010936400003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

722-729

ISBN: 978-989-758-555-5; ISSN: 2184-4321

v3 CNN and achieved 72% accuracy for three classes

and 55% accuracy for nine classes on 129450 images.

(Tschandl et al., 2019) use CNN for lesion segmen-

tation from the HAM10000 dataset (Tschandl et al.,

2018). (Zhang et al., 2019) used an attention resid-

ual learning convolutional neural network evaluated

on the ISIC-skin 2017 dataset (Codella et al., 2018)

with 87% accuracy for the melanoma classiﬁcation.

We use the (ISI, ) skin image database chosen

by Favole et al. (Favole et al., 2020). The au-

thors (Favole et al., 2020) examine the performance

of AlexNet (Krizhevsky et al., 2012), Inception-

V1 (a.k.a. GoogLeNet) (Szegedy et al., 2015) and

Resnet50 (He et al., 2016) CNNs for the classiﬁca-

tion problem of skin lesions.

Our study tests the circular 2D causal auto-

regressive adaptive random (2DSCAR) ﬁeld model

and compares the results with results published in

(Favole et al., 2020). We use 23901 dermoscopic im-

ages of lesions from the ISIC image database. We

have chosen the 2018 JID Editorial, HAM10000,

MSK, SONIC, and UDA datasets. The presented

contribution is the accuracy improvement of skin

melanoma recognition while using a smaller training

set and faster learning than the alternative deep neural

net approach.

2 CIRCULAR MARKOVIAN

TEXTURE REPRESENTATION

Figure 1: The octagonal, circular path. The numbers mark

the site order in which the pixels s, i.e., I

contextual

neighborhoods are traversed. Furthermore, the center pixel

r, for which the statistics are computed, is marked as the

yellow square.

The circular 2D causal auto-regressive adaptive ran-

dom (2DSCAR) ﬁeld model (Fig. 1) is a generaliza-

tion of the directional 2DCAR model (Haindl, 2012)

to the rotationally invariant form, which was intro-

duced in (Reme

s and Haindl, 2018) for bark classiﬁ-

cation application. The model’s contextual neighbor

index shift set denoted I

is functional. The model

for d spectral bands can be deﬁned in the following

matrix form:

= γZ

+ e

, (1)

where γ = [A

, . . . , A

] is the parameter matrix, A

diag[a

, . . . , a

] ∀i, a

i j

are unknown parameters to

be estimated (2), η = cardinality(I

), r = [r

, r

] is

spatial multi-index (r

row and r

column indices) de-

noting history of movements on the lattice I, e

de-

notes the driving white Gaussian noise vector with

zero mean and a constant but unknown covariance

matrix Σ, and Z

is a neighborhood support vec-

tor of multispectral pixels Y

r−s

where s ∈ I

All 2DSCAR model statistics can be efﬁciently

analytically estimated as proven in (Haindl, 2012).

The Bayesian parameter estimation (conditional mean

value)

γ can be accomplished using fast, numerically

robust and recursive statistics (Haindl, 2012), given

the known 2DSCAR process history

(t−1)

= {Y

t−1

, Y

t−2

, . . . , Y

, Z

t−1

, . . . , Z

} :

t−1

= V

−1

zz(t−1)

zy(t−1)

, (2)

t−1

, (3)

t−1



∑

t−1

u=1

∑

t−1

u=1

∑

t−1

u=1

∑

t−1

u=1





yy(t−1)

zy(t−1)

zz(t−1)



, (4)

where V

t−1

is the data gathering matrix, V

a positive deﬁnite initialization matrix (see (Haindl,

2012)). We introduce a new octagonal traversing or-

der multi-index t of the sequence of multi-indices r,

to simplify notation, which depends on the selected

model movement in the underlying lattice I (e.g.,

T = {t

, t

+(1; 0), t

+(2; 0), . . . , t

+(−1; −1)} for

Fig. 1). The optimal functional causal contextual

neighbourhood I

(Fig. 2) can be solved analytically

by a straightforward generalization of the Bayesian

estimate derived in (Haindl, 2012). We did not opti-

mize the neighbourhood I

but used its ﬁxed form

Fig. 2 to simplify and speed up our experiments.

However, if this neighborhood is optimized, we can

expect further accuracy improvement. The model can

be easily applied also to various synthesis and restora-

tion applications. The 2DSCAR model pixel-wise

synthesis is a direct application of the equation (1)

fed from a Gaussian noise generator for any 2DSCAR

model.

2.1 Circular Models

The 2DSCAR model moves (r) on the circular path

on the lattice I as is illustrated in Fig. 1. The causal

Melanoma Recognition

723

→ ↓

· · · • · ·

· • · · · ·

· · • · · ·

· · · · · ·

• · · · · •

· · · · • r

· • · · · ·

· · · · • ·

· · · • · ·

· · · · · •

• · · · · ·

r • · · · ·

← ↑

r • · · · ·

• • · · · •

· · · · · ·

· · · • · ·

· · · · • ·

· · • · · ·

· · · · • r

· · · · · •

• · · · · ·

· · • · · ·

· • · · · ·

· · · · • ·

Figure 2: The applied ﬁxed causal functional contextual

neighbourhood I

in four selected directions, • are the con-

textual neighbours, r is the neigbourhood location index.

Upper left: rightwards (Fig.1-13,14,15), upper right: down-

wards (Fig.1-1,2,3), bottom left leftwards (Fig.1-5,6,7), bot-

tom right upwards (Fig.1-9,10,11), respectively.

neighborhood I

has to be transformed to be con-

sistent for each direction in the traversed path, as de-

noted in Fig. 2. The paths used can be arbitrary as

long as they keep transforming the causal neighbor-

hood into I

in such a way that the model has vis-

ited all neighbors of a control pixel r. Thus these

neighbors are known from the previous steps. We

shall call all these causal paths as circulars further on.

In this paper, we present the octagonal type of path

- (Fig. 1). However, alternatively, a circular path can

be used as well. The parameters for the center pixel

(the yellow square in Fig. 1) of the circular are esti-

mated after the whole path is completed. Since this

model’s equations do not need the whole history of

movement through the image but only the local neigh-

borhood of a single circular, the 2DSCAR models can

be easily parallelized. This memory restriction is ad-

vantageous in comparison to the standard directional

CAR models (Haindl, 2012). The 2DSCAR models

exhibit rotational invariant properties for the circular

shape paths, thanks to the CAR model’s memory of

all the visited pixels. Additional prior contextual in-

formation can be easily incorporated if every initial-

ization matrix V

= V

t−1

, for example, this matrix can

be initialized from the previous data gathering matrix.

2.2 Multispectral Rotationally

Invariant Features

We analyzed the 2DSCAR model around all pixels

with the vertical and horizontal stride of 2 to speed

up the computation for feature extraction. The fol-

lowing α

, α

illumination invariant features ini-

tially derived for the 3DCAR model (Haindl, 2012)

were adapted for the 2DSCAR model:

= 1 + Z

−1

, (5)

∑

−

γZ

)

−1

−

γZ

) , (6)

∑

− µ)

−1

− µ) , (7)

where µ is the mean value of vector Y

and

t−1

= V

yy(t−1)

−V

zy(t−1)

−1

zz(t−1)

. (8)

The inversion data gathering matrix V

−1

zz(t−1)

is up-

dated in its square-root Cholesky factor to guaran-

tee numerical stability for computed model statistics

(Haindl, 2012). Additional used texture features are

also the estimated trace of γ parameters, the poste-

rior probability density (Haindl, 2012)

p(Y

(r−1)

r−1

) = (9)

Γ(

β(r)−η+3

)

Γ(

β(r)−η+2

) π

|λ

(r−1)

(1 + X

−1

x(r−1)

)

1 +

−

r−1

)

−1

(r−1)

−

r−1

)

1 + X

−1

x(r−1)

−

β(r)−η+3

β(r) = r + η − 2, and the absolute error of the one-

step-ahead model prediction (Haindl, 2012):

Abs(GE) =



(r−1)

−Y



−

r−1

. (10)

Fig. 3 illustrates 15 selected features computed from

the HAM database ISIC 0024313 malignant lesion.

3 SKIN LESION CLASSIFIER

The algorithm starts with image subsampling to the

width of 512 px (for larger images) while keeping the

aspect ratio to speed up the feature extraction part.

This subsampling ratio depends on application data;

it is a compromise between the algorithm efﬁciency

and its recognition rate. Every pixel has extracted fea-

tures θ, as described in Sec. 2. The resulting feature

space indexed on the lattice I is assumed to be gov-

erned by the multivariate Gaussian distribution. The

n estimated Gaussian parameters then represent every

training image sample:

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

724

HAM source feature 1 feature 2 feature 4

feature 10 feature 11 feature 12 feature 13

feature 14 feature 18 feature 21 feature 23

feature 24 feature 25 feature 32 feature 33

Figure 3: Examples of the selected 2DSCAR features computed from the HAM ISIC 0024313 malignant texture.

N (θ|µ, Σ) = (11)

(2π)

|Σ|

exp



−

(θ − µ)

−1

(θ − µ)



In the classiﬁcation step, the Gaussian distribu-

tion parameters are estimated for the classiﬁed im-

age in the same way. The classiﬁed image parame-

ters are then compared with all the distributions from

the training samples set using the 1 nearest-neighbor

classiﬁer (1-NN) and the Jeffreys divergence (14) as

the measure. The KL divergence is a probability dis-

tribution non-symmetric similarity measure between

two distributions; it is deﬁned as:

D( f (x)||g(x))

de f

f (x)log

f (x)

g(x)

dx , (12)

where f (x), g(x are the compared probability densi-

ties.

The KL divergence for the Gaussian distribution

data model can be solved analytically:

D( f (x)||g(x)) =



log

|Σ

+tr(Σ

−1

) − d





− µ

)

−1

(µ

− µ



. (13)

We use the Jeffreys divergence, which the sym-

metrized variant of the Kullback-Leibler divergence:

( f (x)||g(x)) =

D( f (x)||g(x)) + D(g(x)|| f (x))

(14)

The selected class is the class of a training image

with the lowest Jeffreys divergence from the tested

image. The primary beneﬁt of our method is the sig-

niﬁcant compression of the training database into the

Gaussian distribution parameters (as we extract only

about n = 40 features, depending on the chosen

neighborhood, we need to store 40 real numbers for

the mean and 40×40 numbers for the covariance ma-

trix). The subsequent comparison with the training

Melanoma Recognition

725

2018 JID m (ISIC 0024233) 2018 JID m (ISIC 0024284) 2018 JID u (ISIC 0024209)

HAM m (ISIC 0024313) HAM b (ISIC 0024306) HAM u (ISIC 0024318)

MSK m (ISIC 0011460) MSK b (ISIC 0011409) MSK u (ISIC 0011444)

SONIC b (ISIC 0000579) SONIC b (ISIC 0000557) SONIC b (ISIC 0003588)

UDA m (ISIC 0000002) UDA b (ISIC 0000000) UDA b (ISIC 0000020)

Figure 4: Examples of skin lesion images (m - malignant, b - benign, u - unknown) from the used datasets.

database is thus extremely fast, enabling us to com-

pare hundreds of thousands of image feature distribu-

tions per second on an ordinary computer.

4 EXPERIMENTAL SKIN DATA

We veriﬁed the proposed method on the publicly

available skin image International Skin Imaging Col-

laboration Archive (ISIC) database (ISI, ; Rotemberg

et al., 2021). We used the following datasets from the

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

726

Table 1: Used datasets.

Dataset No. of images Image resolution

[pixels]

2018 JID Editorial 100 various

HAM10000 10 015 600 × 450

MSK 3 918 various

SONIC 9 251 3024 × 2016

UDA 617 various

total 23 901

ISIC archive (Tab. 1) to be able to compare our results

with the results published in (Favole et al., 2020):

The 2018 JID Editorial dataset contains selected

100 sequentially biopsied cutaneous melanomas (37),

basal cell carcinomas (40), and squamous cell

carcinomas (23) with high-quality clinical images

(Navarrete-Dechent et al., 2018) from the ISIC

archive. All lesions originated from Caucasian pa-

tients in the southern United States. Each lesion in

the skin datasets is represented by a single image in

the JPEG format.

The HAM10000 dataset (Tschandl et al., 2018) is

made up of 10 015 images and was collected over 20

years. Thus, the older images were digitized from

slides with a Nikon Coolscan 5000 ED scanner in

300 DPI resolution. The more recent images were

acquired from the digital dermatoscopy system Mole-

Max HD or the DermLiteTM Foto camera.

The MSK dataset (Codella et al., 2018) which

contains ﬁve subsets made up of 3918 in total. This

set with 3 to 5 images per lesion was acquired using

a dermoscopic attachment to either a digital single re-

ﬂex lens (SLR) camera or a smartphone (Rotemberg

et al., 2021).

The SONIC dataset (Gomez and Herrera, 2017)

contains 9251 images from SONIC Healthcare, Aus-

tralia, which is acquired with Fujiﬁlm FinePix S2 Pro

and 176 Nikon D300 cameras.

The UDA dataset contains two subsets which are

made up of 617 images. It includes melanoma and

benign lesions with a histopathological diagnosis or

clinically benign history containing metadata with pa-

tient age, diagnosis, gender, and anatomic location.

The ISIC data is highly unbalanced, with 80% be-

nign, 10% malignant, and 10% unknown images, this

severe underrepresentation of the most common skin

lesions can lead to a much lower diagnostic accuracy

when many samples are wrongly assigned to the same

class. Authors (Favole et al., 2020) tried to prevent

this problem by adding an altered version of malig-

nant and unknown images. We did not use such help

to see if our features can outperform the deep neural

net results even with a much smaller learning data set.

Fig. 4 illustrates the malignant, benign, or un-

known image examples of all ﬁve datasets. We have

used the leave-one-out approach for the classiﬁcation

rate estimation and only on the original dataset with-

out any data augmentation as was done in the com-

pared deep neural net results (Favole et al., 2020).

Although in (Favole et al., 2020) the ratio of training

and testing sets is 80:20, due to the data augmenta-

tion, they still use the double size training set than the

presented method. Thus both validation approaches

(hold-out and leave-one-out) can be compared. The

augmented data in their validation set ((Favole et al.,

2020)) artiﬁcially increased their accuracy. Thus our

improvement in Tab. 3 would be in the correct com-

parison even higher.

Table 2: The 2DSCAR method results for used skin

datasets.

Benign Malignant Unknow Precision

gr. truth gr. truth gr. truth [%]

Benign int. 17941 858 454 93.2

Malignant int. 965 1175 279 48.6

Unknown int. 467 252 1510 67.7

Sensitivity Accuracy

[%] 92.6 51.4 67.3 86.3

5 RESULTS

Our experiment compares three separate lesion

classes - benign, malignant, and unknown images us-

ing a single resolution level. We have reached 86.3%

accuracy on the selected dataset. The sensitivity for

all classes is between 51.4 − 92.6 [%] with median

value 67.3% and precision 48.6 − 93.2 [%] with me-

dian value 67.7%. More details about the results are

in Tab. 2. If we added all ﬂipped versions of malig-

nant and unknown images as in (Favole et al., 2020),

the test database would contain 52% benign, 24% ma-

lignant, and 24% unknown images, and the total num-

ber of images in the test database is 37485. The ac-

curacy would be improved to 95% and signiﬁcantly

in malignant (precision 94%, sensitivity 99.7%) and

unknown images (precision 88%, sensitivity 99.7%).

We compared the accuracy results of the 2DSCAR

with the three CNNs evaluated in (Favole et al., 2020)

and additional three recently published CNN meth-

ods. The comparative results are in Tab. 3 with

each method’s number of parameters. The presented

method has three order fewer parameters than the al-

ternative CNNs, which means that our method needs

much less data for our reliable model learning. The

training times reported in (Favole et al., 2020) are be-

tween 47 minutes for the fastest Inception-V1 until 72

Melanoma Recognition

727

Table 3: Accuracy comparison. Results denoted

∗

use pre-trained nets with transfer learning.

Method No. of parameters Accuracy Precision Sensitivity Speciﬁcity

2DSCAR 1.6 · 10

0.86 0.70 0.71 0.87

2DSCAR augmented 1.6 · 10

0.95 0.94 0.97 0.98

AlexNet (Favole et al., 2020) 6 · 10

0.74 - - -

Inception-V1 (Favole et al., 2020) 5 · 10

0.70 - - -

RestNet50 (Favole et al., 2020) 2.6 · 10

0.74 - 0.75 0.86

ARL-CNN50

∗

(Zhang et al., 2019) 2.3 · 10

0.86 - 0.77 0.88

R-CNN

∗

(Khan et al., 2021) 2.3 · 10

0.86 0.87 0.86 -

Hosny

∗

(Hosny et al., 2020) 5 · 10

0.98 - 0.98 0.99

minutes for RestNet50. We list the results of the last

three methods (

∗

) for broad outline only. These meth-

ods cannot be directly compared because their authors

do not specify their training data and other parameters

precisely.

Although we eliminated variable illumination and

the observation rotation problem using the illumina-

tion and rotationally invariant features in our study,

other challenges, such as viewing angles, body posi-

tion, observation distance, resolution, or variability in

acquisition technologies, need to be treated for reli-

able and fully automatic skin diagnosis system. We

plan to investigate these research problems in our fu-

ture studies.

6 CONCLUSION

We present the method for lesion image catego-

rization. The classiﬁer uses rotationally invariant

monospectral Markovian textural features from all

three spectral classes. Our textural features are analyt-

ically derived from the underlying descriptive textural

model and can be efﬁciently, recursively, and adap-

tively learned. Our 2DSCAR features are rotationally

invariant, exploit information from all spectral bands,

and can be easily parallelized or made fully illumi-

nation invariant if the non-illumination invariant fea-

tures are excluded (the posterior probability density

and the absolute error of the one-step-ahead predic-

tion). The classiﬁer does not need extensive learn-

ing data contrary to the convolutional neural nets and

outperforms in classiﬁcation accuracy three deep net-

work nets on the same data set with more than 12%.

If we add ﬂipped versions of the malignant and un-

known images for learning as was done with the al-

ternative three deep network nets, we could outper-

form these methods up to 20%. Another beneﬁt of

the presented method is the order of magnitude faster

learning.

ACKNOWLEDGMENT

The Czech Science Foundation project GA

CR 19-

12340S supported this research.

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